task.py 44.3 KB
Newer Older
1
import abc
2
from dataclasses import dataclass, field, asdict
3
4

import re
5
import ast
lintangsutawika's avatar
lintangsutawika committed
6
import yaml
7
8
9
import evaluate
import random
import itertools
10
import functools
11
from tqdm import tqdm
12
13
14
15

import datasets
import numpy as np

baberabb's avatar
baberabb committed
16
from typing import Union, List, Any, Tuple, Literal
17
from collections.abc import Callable
18

19
from lm_eval import utils
20
from lm_eval.api import samplers
haileyschoelkopf's avatar
haileyschoelkopf committed
21
from lm_eval.api.instance import Instance
lintangsutawika's avatar
lintangsutawika committed
22
from lm_eval.api.filter import FilterEnsemble
23
24
25
26

from lm_eval.logger import eval_logger
from lm_eval.prompts import get_prompt
from lm_eval.filters import build_filter_ensemble
lintangsutawika's avatar
lintangsutawika committed
27
28
29
30
from lm_eval.api.metrics import (
    mean,
    weighted_perplexity,
    bits_per_byte,
lintangsutawika's avatar
lintangsutawika committed
31
    metric_max_over_ground_truths,
lintangsutawika's avatar
lintangsutawika committed
32
33
)
from lm_eval.api.registry import (
haileyschoelkopf's avatar
haileyschoelkopf committed
34
35
36
37
    get_metric,
    get_aggregation,
    get_default_aggregation,
    is_higher_better,
38
39
    DEFAULT_METRIC_REGISTRY,
    OUTPUT_TYPE_REGISTRY,
lintangsutawika's avatar
lintangsutawika committed
40
41
    AGGREGATION_REGISTRY,
)
42

43
44
45
46
47
48
49
ALL_OUTPUT_TYPES = [
    "loglikelihood",
    "multiple_choice",
    "loglikelihood_rolling",
    "greedy_until",
]

50
51
52

@dataclass
class TaskConfig(dict):
53
    # task naming/registry
54
    task: str = None
55
    group: Union[str, list] = None
56
57
58
    # HF dataset options.
    # which dataset to use,
    # and what splits for what purpose
59
60
    dataset_path: str = None
    dataset_name: str = None
61
    dataset_kwargs: dict = None
62
63
64
    training_split: str = None
    validation_split: str = None
    test_split: str = None
lintangsutawika's avatar
lintangsutawika committed
65
    fewshot_split: str = None  # TODO: assert that this not None if num_fewshot > 0. (?) assert if this is same split as one evaling (?)
66
67
    # formatting / prompting options.
    # see docs/advanced_task_guide.md for more info
68
    process_docs: Callable = None
69
70
    doc_to_text: Union[Callable, str] = None
    doc_to_target: Union[Callable, str] = None
lintangsutawika's avatar
lintangsutawika committed
71
    doc_to_choice: Union[Callable, str, dict, list] = None
72
    gold_alias: Union[Callable, str] = None
lintangsutawika's avatar
lintangsutawika committed
73
    process_results: Union[Callable, str] = None
74
    use_prompt: str = None
75
    description: str = ""
76
77
    target_delimiter: str = " "
    fewshot_delimiter: str = "\n\n"
78
    # runtime configuration options
79
    num_fewshot: int = 0
80
    # scoring options
81
82
    metric_list: str = None
    output_type: str = "greedy_until"
83
    generation_kwargs: dict = None
84
    repeats: int = 1
lintangsutawika's avatar
lintangsutawika committed
85
    filter_list: Union[str, list] = None
86
87
    should_decontaminate: bool = False
    doc_to_decontamination_query: str = None
88

lintangsutawika's avatar
lintangsutawika committed
89
    metadata: str = None  # by default, not used in the code. allows for users to pass arbitrary info to tasks
90

91
    def __post_init__(self):
92

Lintang Sutawika's avatar
Lintang Sutawika committed
93
94
95
        if self.generation_kwargs is not None:
            if self.output_type != "greedy_until":
                eval_logger.warning(
96
                    "passed `generation_kwargs`, but not using `output_type: greedy_until`!"
Lintang Sutawika's avatar
Lintang Sutawika committed
97
                )
98
                assert self.output_type != "greedy_until"
Lintang Sutawika's avatar
Lintang Sutawika committed
99
100
101
102
103
104
105

            if "temperature" in self.generation_kwargs:
                self.generation_kwargs["temperature"] = float(
                    self.generation_kwargs["temperature"]
                )

            if "until" not in self.generation_kwargs:
106
                self.generation_kwargs["until"] = [self.fewshot_delimiter]
Lintang Sutawika's avatar
Lintang Sutawika committed
107
108
109
110
        else:
            if self.output_type == "greedy_until":
                # ensure that we greedily generate in absence of explicit arguments otherwise
                self.generation_kwargs = {
Lintang Sutawika's avatar
Lintang Sutawika committed
111
                    "until": None
112
113
                    if self.fewshot_delimiter is None
                    else [self.fewshot_delimiter],
Lintang Sutawika's avatar
Lintang Sutawika committed
114
115
116
                    "do_sample": False,
                    "temperature": 0.0,
                }
117

haileyschoelkopf's avatar
haileyschoelkopf committed
118
119
        # TODO: how to make TaskConfigs be de- and re-serializable, even when using the !function constructor?

120
121
122
    def __getitem__(self, item):
        return getattr(self, item)

123
124
125
    def __setitem__(self, item, value):
        return setattr(self, item, value)

126
    def to_dict(self):
127
128
        """dumps the current config as a dictionary object, as a printable format.
        null fields will not be printed.
haileyschoelkopf's avatar
haileyschoelkopf committed
129
        Used for dumping results alongside full task configuration
130

haileyschoelkopf's avatar
haileyschoelkopf committed
131
132
133
134
135
136
137
138
139
140
        :return: dict
            A printable dictionary version of the TaskConfig object.

        # TODO: should any default value in the TaskConfig not be printed?
        """
        cfg_dict = asdict(self)
        # remove values that are `None`
        for k, v in list(cfg_dict.items()):
            if v is None:
                cfg_dict.pop(k)
haileyschoelkopf's avatar
haileyschoelkopf committed
141
142
143
            elif isinstance(v, Callable):
                # TODO: this should handle Promptsource template objects as a separate case?
                cfg_dict[k] = str(v)
haileyschoelkopf's avatar
haileyschoelkopf committed
144
        return cfg_dict
145

146
147
148
149
150
151
152
153
154
155
156
157

class Task(abc.ABC):
    """A task represents an entire benchmark including its dataset, problems,
    answers, and evaluation methods. See BoolQ for a simple example implementation

    A `doc` can be any python object which represents one instance of evaluation.
    This is usually a dictionary e.g.
        {"question": ..., "answer": ...} or
        {"question": ..., question, answer)
    """

    VERSION = None
158

159
160
161
162
163
164
165
166
    # The name of the `Task` benchmark as denoted in the HuggingFace datasets Hub
    # or a path to a custom `datasets` loading script.
    DATASET_PATH: str = None

    # The name of a subset within `DATASET_PATH`.
    DATASET_NAME: str = None

    OUTPUT_TYPE: str = None
lintangsutawika's avatar
lintangsutawika committed
167

168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
    def __init__(
        self,
        data_dir=None,
        cache_dir=None,
        download_mode=None,
        config=None,
    ):
        """
        :param data_dir: str
            Stores the path to a local folder containing the `Task`'s data files.
            Use this to specify the path to manually downloaded data (usually when
            the dataset is not publicly accessible).
        :param cache_dir: str
            The directory to read/write the `Task` dataset. This follows the
            HuggingFace `datasets` API with the default cache directory located at:
                `~/.cache/huggingface/datasets`
            NOTE: You can change the cache location globally for a given process
            by setting the shell environment variable, `HF_DATASETS_CACHE`,
            to another directory:
                `export HF_DATASETS_CACHE="/path/to/another/directory"`
        :param download_mode: datasets.DownloadMode
            How to treat pre-existing `Task` downloads and data.
            - `datasets.DownloadMode.REUSE_DATASET_IF_EXISTS`
                Reuse download and reuse dataset.
            - `datasets.DownloadMode.REUSE_CACHE_IF_EXISTS`
                Reuse download with fresh dataset.
            - `datasets.DownloadMode.FORCE_REDOWNLOAD`
                Fresh download and fresh dataset.
        """
        self.download(data_dir, cache_dir, download_mode)
        self._training_docs = None
        self._fewshot_docs = None
        self._instances = None

haileyschoelkopf's avatar
haileyschoelkopf committed
202
        self._config = TaskConfig(**config) if config else TaskConfig()
203
204
205

        if not hasattr(self, "_filters"):
            self._filters = []
lintangsutawika's avatar
lintangsutawika committed
206
            for name, components in self._config.get(
207
                "filters", [["none", [["take_first", None]]]]
lintangsutawika's avatar
lintangsutawika committed
208
            ):
209
210
211
                filter_pipeline = build_filter_ensemble(name, components)
                self._filters.append(filter_pipeline)

lintangsutawika's avatar
lintangsutawika committed
212
        self.sampler = samplers.Sampler(
213
214
            list(self.fewshot_docs()), self, rnd=random.Random(1234)
        )
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240

    def download(self, data_dir=None, cache_dir=None, download_mode=None):
        """Downloads and returns the task dataset.
        Override this method to download the dataset from a custom API.

        :param data_dir: str
            Stores the path to a local folder containing the `Task`'s data files.
            Use this to specify the path to manually downloaded data (usually when
            the dataset is not publicly accessible).
        :param cache_dir: str
            The directory to read/write the `Task` dataset. This follows the
            HuggingFace `datasets` API with the default cache directory located at:
                `~/.cache/huggingface/datasets`
            NOTE: You can change the cache location globally for a given process
            by setting the shell environment variable, `HF_DATASETS_CACHE`,
            to another directory:
                `export HF_DATASETS_CACHE="/path/to/another/directory"`
        :param download_mode: datasets.DownloadMode
            How to treat pre-existing `Task` downloads and data.
            - `datasets.DownloadMode.REUSE_DATASET_IF_EXISTS`
                Reuse download and reuse dataset.
            - `datasets.DownloadMode.REUSE_CACHE_IF_EXISTS`
                Reuse download with fresh dataset.
            - `datasets.DownloadMode.FORCE_REDOWNLOAD`
                Fresh download and fresh dataset.
        """
241
242
243
244
245
246
247
        self.dataset = datasets.load_dataset(
            path=self.DATASET_PATH,
            name=self.DATASET_NAME,
            data_dir=data_dir,
            cache_dir=cache_dir,
            download_mode=download_mode,
        )
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284

    @abc.abstractmethod
    def has_training_docs(self):
        """Whether the task has a training set"""
        pass

    @abc.abstractmethod
    def has_validation_docs(self):
        """Whether the task has a validation set"""
        pass

    @abc.abstractmethod
    def has_test_docs(self):
        """Whether the task has a test set"""
        pass

    def training_docs(self):
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        return []

    def validation_docs(self):
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        return []

    def test_docs(self):
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        return []

285
286
287
288
289
290
291
292
293
294
    def fewshot_docs(self):
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        if self.has_training_docs():
            return self.training_docs()
        elif self.has_validation_docs():
            return self.validation_docs()
        else:
lintangsutawika's avatar
lintangsutawika committed
295
            eval_logger.warning(
296
                "has_training_docs and has_validation_docs are False"
297
                ", using test_docs as fewshot_docs but this is not recommended."
lintangsutawika's avatar
lintangsutawika committed
298
            )
299
300
            return self.test_docs()

301
302
303
304
305
306
307
308
309
310
    def _process_doc(self, doc):
        """
        Override this to process (detokenize, strip, replace, etc.) individual
        documents. This can be used in a map over documents of a data split.
        E.g. `map(self._process_doc, self.dataset["validation"])`

        :return: dict
            The processed version of the specified `doc`.
        """
        return doc
lintangsutawika's avatar
lintangsutawika committed
311

312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
    @property
    def instances(self):
        """After calling `task.build_all_requests()`, tasks
        maintain a list of the dataset instances which will be evaluated.
        """
        return self._instances

    def fewshot_examples(self, k, rnd):
        if self._training_docs is None:
            self._training_docs = list(self.training_docs())

        return rnd.sample(self._training_docs, k)

    def doc_to_decontamination_query(self, doc):
        print(
            "Override doc_to_decontamination_query with document specific decontamination query."
        )
        assert False

    @abc.abstractmethod
    def doc_to_text(self, doc):
        pass

    @abc.abstractmethod
    def doc_to_target(self, doc):
        pass

339
    def build_all_requests(self, limit=None, rank=None, world_size=None):
340
341
342
343
344
345
346
347
348
349
        """Build a set of Instances for a task, and store them in task.instances"""
        if self.has_test_docs():
            docs = self.test_docs()
        elif self.has_validation_docs():
            docs = self.validation_docs()
        else:
            assert (
                False
            ), f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!"

350
351
352
353
        eval_logger.info(
            f"Building contexts for task '{self._config.task}' on rank {rank}..."
        )

354
        instances = []
355
356
        for doc_id, doc in utils.create_iterator(
            enumerate(docs), rank, world_size, limit
lintangsutawika's avatar
lintangsutawika committed
357
        ):
358
            # sample fewshot context #TODO: need to offset doc_id by rank now!
359
            fewshot_ctx = self.fewshot_context(
360
361
                doc,
                self._config.num_fewshot,
362
            )
363

haileyschoelkopf's avatar
haileyschoelkopf committed
364
            # TODO: we should override self._config.repeats if doing greedy gen so users don't waste time+compute
lintangsutawika's avatar
lintangsutawika committed
365
366
367
            inst = self.construct_requests(
                doc=doc,
                ctx=fewshot_ctx,
368
                metadata=(self._config["task"], doc_id, self._config.repeats),
lintangsutawika's avatar
lintangsutawika committed
369
            )
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394

            if not isinstance(inst, list):
                inst = [inst]

            instances.extend(inst)

        self._instances = instances
        assert len(self._instances) != 0, "task.build_requests() did not find any docs!"

    @abc.abstractmethod
    def construct_requests(self, doc, ctx, **kwargs):
        """Uses RequestFactory to construct Requests and returns an iterable of
        Requests which will be sent to the LM.

        :param doc:
            The document as returned from training_docs, validation_docs, or test_docs.
        :param ctx: str
            The context string, generated by fewshot_context. This includes the natural
            language description, as well as the few shot examples, and the question
            part of the document for `doc`.
        :param doc_idx: int
            The index of a document within `self.test_docs()` or `self.validation_docs()`,
            whichever is the main split used.
        :param repeats: int
        TODO: update this docstring
lintangsutawika's avatar
lintangsutawika committed
395
            The number of times each instance in a dataset is inferred on. Defaults to 1,
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
            can be increased for techniques like majority voting.
        """
        pass

    @abc.abstractmethod
    def process_results(self, doc, results):
        """Take a single document and the LM results and evaluates, returning a
        dict where keys are the names of submetrics and values are the values of
        the metric for that one document

        :param doc:
            The document as returned from training_docs, validation_docs, or test_docs.
        :param results:
            The results of the requests created in construct_requests.
        """
        pass

    @abc.abstractmethod
    def aggregation(self):
        """
        :returns: {str: [metric_score] -> float}
            A dictionary where keys are the names of submetrics and values are
            functions that aggregate a list of metric scores
        """
        pass

    @abc.abstractmethod
    def higher_is_better(self):
        """
        :returns: {str: bool}
            A dictionary where keys are the names of submetrics and values are
            whether a higher value of the submetric is better
        """
        pass

haileyschoelkopf's avatar
haileyschoelkopf committed
431
432
433
434
435
436
437
438
439
440
    @classmethod
    def count_bytes(cls, doc):
        """Used for byte-level perplexity metrics in rolling loglikelihood"""
        return len(doc.encode("utf-8"))

    @classmethod
    def count_words(cls, doc):
        """Downstream loglikelihood_rolling perplexity tasks with custom word boundaries should override this!"""
        return len(re.split(r"\s+", doc))

441
    @utils.positional_deprecated
442
    def fewshot_context(self, doc, num_fewshot):
443
444
445
446
447
448
449
450
451
452
453
454
        """Returns a fewshot context string that is made up of a prepended description
        (if provided), the `num_fewshot` number of examples, and an appended prompt example.

        :param doc: str
            The document as returned from training_docs, validation_docs, or test_docs.
        :param num_fewshot: int
            The number of fewshot examples to provide in the returned context string.
        :returns: str
            The fewshot context.
        """

        if num_fewshot == 0:
455
456
            # always prepend the (possibly empty) task description
            labeled_examples = self._config.description
457
        else:
lintangsutawika's avatar
lintangsutawika committed
458
459
460
            labeled_examples = self._config.description + self.sampler.get_context(
                doc, num_fewshot
            )
461
462

        example = self.doc_to_text(doc)
463
464
465
466
        if type(example) == str:
            return labeled_examples + example
        elif type(example) == list:
            return [labeled_examples + ex for ex in example]
467
        elif type(example) == int:
lintangsutawika's avatar
lintangsutawika committed
468
469
470
471
472
            if self._config.doc_to_choice is not None:
                choices = self.doc_to_choice(doc)
                return labeled_examples + choices[example]
            else:
                return labeled_examples + str(example)
473
474
475

    def apply_filters(self):

lintangsutawika's avatar
lintangsutawika committed
476
477
478
479
480
481
        if hasattr(self, "_filters"):
            for f in self._filters:
                f.apply(self._instances)
        else:
            eval_logger.warning("No filter defined, passing through instances")
            return self._instances
482

baberabb's avatar
baberabb committed
483
    def dump_config(self) -> dict:
484
        """Returns a dictionary representing the task's config.
485
486
487
488
489

        :returns: str
            The fewshot context.
        """
        # TODO: this should only return the overrides applied to a non-YAML task's configuration.
490
        # (num_fewshot)
491
492
        return self._config.to_dict()

493
494

class ConfigurableTask(Task):
495
    VERSION = "Yaml"
496
    OUTPUT_TYPE = None
497
    CONFIG = None
498
499
500

    def __init__(
        self, data_dir=None, cache_dir=None, download_mode=None, config: dict = None
baberabb's avatar
baberabb committed
501
    ):  # TODO no super() call here
502
        # Get pre-configured attributes
503
        self._config = self.CONFIG
504

505
506
        # Use new configurations if there was no preconfiguration
        if self._config is None:
507
            self._config = TaskConfig(**config)
508
509
        # Overwrite configs
        else:
lintangsutawika's avatar
lintangsutawika committed
510
            if config is not None:
511
                self._config.__dict__.update(config)
512

513
        if self._config is None:
lintangsutawika's avatar
lintangsutawika committed
514
515
516
            raise ValueError(
                "Must pass a config to ConfigurableTask, either in cls.CONFIG or `config` kwarg"
            )
517
518

        if self._config.output_type is not None:
519
            assert self._config.output_type in ALL_OUTPUT_TYPES
520
521
            self.OUTPUT_TYPE = self._config.output_type

522
523
524
525
526
527
        if self._config.dataset_path is not None:
            self.DATASET_PATH = self._config.dataset_path

        if self._config.dataset_name is not None:
            self.DATASET_NAME = self._config.dataset_name

528
529
530
531
        self._metric_fn_list = {}
        self._metric_fn_kwargs = {}
        self._aggregation_list = {}
        self._higher_is_better = {}
532

533
        _metric_list = DEFAULT_METRIC_REGISTRY[self._config.output_type]
534
        if self._config.metric_list is None:
535
            # TODO: handle this in TaskConfig.__post_init__ ?
536
            for metric_name in _metric_list:
haileyschoelkopf's avatar
haileyschoelkopf committed
537
538
                self._metric_fn_list[metric_name] = get_metric(metric_name)
                self._aggregation_list[metric_name] = get_default_aggregation(
539
                    metric_name
haileyschoelkopf's avatar
haileyschoelkopf committed
540
541
                )
                self._higher_is_better[metric_name] = is_higher_better(metric_name)
542
543
544
545
546
547
548
549
550
        else:
            for metric_config in self._config.metric_list:
                assert "metric" in metric_config
                metric_name = metric_config["metric"]
                kwargs = {
                    key: metric_config[key]
                    for key in metric_config
                    if key not in ["metric", "aggregation", "higher_is_better"]
                }
551

552
                if self._config.process_results is not None:
553
554
                    self._metric_fn_list[metric_name] = None
                    self._metric_fn_kwargs[metric_name] = {}
555
556
557
558
559
560
561
562
                elif callable(metric_name):
                    metric_fn = metric_name.__call__
                    metric_name = metric_name.__name__
                    self._metric_fn_list[metric_name] = metric_fn
                    self._metric_fn_kwargs[metric_name] = kwargs
                else:
                    self._metric_fn_list[metric_name] = get_metric(metric_name)
                    self._metric_fn_kwargs[metric_name] = kwargs
lintangsutawika's avatar
lintangsutawika committed
563

564
                if "aggregation" in metric_config:
565
                    agg_name = metric_config["aggregation"]
566
                    if type(agg_name) == str:
haileyschoelkopf's avatar
haileyschoelkopf committed
567
                        self._aggregation_list[metric_name] = get_aggregation(agg_name)
568
569
570
571
                    elif callable(agg_name):
                        self._aggregation_list[metric_name] = metric_config[
                            "aggregation"
                        ]
572
                else:
573
574

                    INV_AGG_REGISTRY = {v: k for k, v in AGGREGATION_REGISTRY.items()}
haileyschoelkopf's avatar
haileyschoelkopf committed
575
                    metric_agg = get_default_aggregation(metric_name)
576
                    eval_logger.warning(
577
578
579
                        f"metric {metric_name} is defined, but aggregation is not. "
                        f"using default "
                        f"aggregation={INV_AGG_REGISTRY[metric_agg]}"
580
                    )
581
                    self._aggregation_list[metric_name] = metric_agg
lintangsutawika's avatar
lintangsutawika committed
582

583
584
585
586
587
588
                if "higher_is_better" in metric_config:
                    self._higher_is_better[metric_name] = metric_config[
                        "higher_is_better"
                    ]
                else:
                    eval_logger.warning(
589
590
                        f"metric {metric_name} is defined, but higher_is_better is not. "
                        f"using default "
haileyschoelkopf's avatar
haileyschoelkopf committed
591
                        f"higher_is_better={is_higher_better(metric_name)}"
592
                    )
haileyschoelkopf's avatar
haileyschoelkopf committed
593
                    self._higher_is_better[metric_name] = is_higher_better(metric_name)
594

595
        self.download(self._config.dataset_kwargs)
596
597
598
        self._training_docs = None
        self._fewshot_docs = None

lintangsutawika's avatar
lintangsutawika committed
599
        if self._config.filter_list is not None:
lintangsutawika's avatar
lintangsutawika committed
600
            self._filters = []
lintangsutawika's avatar
lintangsutawika committed
601
602
603
604
605
606
607
608
            for filter_config in self._config.filter_list:
                for filter_pipeline in filter_config:
                    filter_name = filter_config["name"]
                    filter_functions = filter_config["filter"]
                    components = []
                    for function in filter_functions:
                        kwargs = {
                            key: function[key] for key in function if key != "function"
lintangsutawika's avatar
lintangsutawika committed
609
610
611
                        }
                        components.append([function["function"], kwargs])
                    filter_pipeline = build_filter_ensemble(filter_name, components)
lintangsutawika's avatar
lintangsutawika committed
612
                self._filters.append(filter_pipeline)
lintangsutawika's avatar
lintangsutawika committed
613
        else:
614
            self._filters = [build_filter_ensemble("none", [["take_first", None]])]
615
616

        if self._config.use_prompt is not None:
lintangsutawika's avatar
lintangsutawika committed
617
            eval_logger.info(f"loading prompt {self._config.use_prompt}")
618
            self.prompt = get_prompt(
lintangsutawika's avatar
lintangsutawika committed
619
620
                self._config.use_prompt, self.DATASET_PATH, self.DATASET_NAME
            )
621
622
623
        else:
            self.prompt = None

lintangsutawika's avatar
lintangsutawika committed
624
625
        if self.fewshot_docs() is not None:
            self.sampler = samplers.Sampler(
626
                list(self.fewshot_docs()), self, rnd=random.Random(1234)
627
            )
628

629
        if self.has_test_docs():
630
            self.task_docs = self.test_docs()
631
        elif self.has_validation_docs():
632
            self.task_docs = self.validation_docs()
633
634
635
636
637
        else:
            assert (
                False
            ), f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!"

638
        # Test One Doc
639
        self.features = list(self.task_docs.features.keys())
640
641
        self.multiple_input = 0
        self.multiple_target = 0
642
        test_doc = self.task_docs[0]
643
        test_text = self.doc_to_text(test_doc)
644
        test_target = self.doc_to_target(test_doc)
lintangsutawika's avatar
lintangsutawika committed
645
646
647
648
649

        if self._config.doc_to_choice is not None:
            test_choice = self.doc_to_choice(test_doc)
            if type(test_choice) is not list:
                eval_logger.error("doc_to_choice must return list")
650
651
            else:
                num_choice = len(test_choice)
652

653
654
            if type(test_text) is int:
                self.multiple_input = num_choice
lintangsutawika's avatar
lintangsutawika committed
655
656
        else:
            test_choice = None
657

658
        if type(test_target) is list:
659
            self.multiple_target = len(test_target)
lintangsutawika's avatar
lintangsutawika committed
660
661
662
663
664
        else:
            if (type(test_target) is int) and (test_choice is not None):
                test_target = test_choice[test_target]
            else:
                test_target = str(test_target)
665

lintangsutawika's avatar
lintangsutawika committed
666
667
668
        if test_choice is not None:
            check_choices = test_choice
        else:
lintangsutawika's avatar
lintangsutawika committed
669
            check_choices = [test_target]
lintangsutawika's avatar
lintangsutawika committed
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685

        for choice in check_choices:
            choice_has_whitespace = True if " " in choice else False
            delimiter_has_whitespace = (
                True if " " in self._config.target_delimiter else False
            )

            if delimiter_has_whitespace and choice_has_whitespace:
                eval_logger.warning(
                    f'Both target_delimiter and target choice: "{choice}" have whitespace'
                )
            elif (not delimiter_has_whitespace) and (not choice_has_whitespace):
                eval_logger.warning(
                    f'Both target_delimiter and target choice: "{choice}" does not have whitespace, ignore if the language you are evaluating on does not require/use whitespace'
                )

686
687
688
689
690
691
692
693
    def download(self, dataset_kwargs=None):

        self.dataset = datasets.load_dataset(
            path=self.DATASET_PATH,
            name=self.DATASET_NAME,
            **dataset_kwargs if dataset_kwargs is not None else {},
        )

baberabb's avatar
baberabb committed
694
    def has_training_docs(self) -> bool:
695
696
697
698
699
        if self._config.training_split is not None:
            return True
        else:
            return False

baberabb's avatar
baberabb committed
700
    def has_validation_docs(self) -> bool:
701
702
703
704
705
        if self._config.validation_split is not None:
            return True
        else:
            return False

baberabb's avatar
baberabb committed
706
    def has_test_docs(self) -> bool:
707
708
709
710
711
        if self._config.test_split is not None:
            return True
        else:
            return False

baberabb's avatar
baberabb committed
712
    def training_docs(self) -> datasets.Dataset:
713
        if self.has_training_docs():
714
            if self._config.process_docs is not None:
715
716
717
                return self._config.process_docs(
                    self.dataset[self._config.training_split]
                )
718
719
            return self.dataset[self._config.training_split]

baberabb's avatar
baberabb committed
720
    def validation_docs(self) -> datasets.Dataset:
721
        if self.has_validation_docs():
722
            if self._config.process_docs is not None:
723
724
725
                return self._config.process_docs(
                    self.dataset[self._config.validation_split]
                )
726
727
            return self.dataset[self._config.validation_split]

baberabb's avatar
baberabb committed
728
    def test_docs(self) -> datasets.Dataset:
729
        if self.has_test_docs():
730
            if self._config.process_docs is not None:
731
                return self._config.process_docs(self.dataset[self._config.test_split])
732
733
            return self.dataset[self._config.test_split]

734
    def fewshot_docs(self):
735
        if self._config.fewshot_split is not None:
736
            return self.dataset[self._config.fewshot_split]
737
738
739
        else:
            if self._config.num_fewshot > 0:
                eval_logger.warning(
haileyschoelkopf's avatar
haileyschoelkopf committed
740
                    f"Task '{self._config.task}': "
741
742
743
744
                    "num_fewshot > 0 but fewshot_split is None. "
                    "using preconfigured rule."
                )
            return super().fewshot_docs()
745

746
747
748
749
750
751
752
753
754
    def apply_filters(self):

        if hasattr(self, "_filters"):
            for f in self._filters:
                f.apply(self._instances, self.task_docs)
        else:
            eval_logger.warning("No filter defined, passing through instances")
            return self._instances

755
756
757
758
759
    def should_decontaminate(self):
        return self._config.should_decontaminate

    def doc_to_decontamination_query(self, doc):
        if self._config.should_decontaminate:
760
761
762
763
764
765
            if self._config.doc_to_decontamination_query in self.features:
                return doc[self._config.doc_to_decontamination_query]
            else:
                return ast.literal_eval(
                    utils.apply_template(self._config.doc_to_decontamination_query, doc)
                )
766

767
768
769
770
771
772
773
774
775
776
777
778
    def _process_doc(self, doc):
        """
        Override this to process (detokenize, strip, replace, etc.) individual
        documents. This can be used in a map over documents of a data split.
        E.g. `map(self._process_doc, self.dataset["validation"])`

        :return: dict
            The processed version of the specified `doc`.
        """
        return doc

    def doc_to_text(self, doc):
779
780
781

        if self.prompt is not None:
            doc_to_text = self.prompt
782
783
        else:
            doc_to_text = self._config.doc_to_text
lintangsutawika's avatar
lintangsutawika committed
784

785
786
787
        if type(doc_to_text) == int:
            return doc_to_text
        elif type(doc_to_text) == str:
788
            if doc_to_text in self.features:
789
790
791
                # if self._config.doc_to_choice is not None:
                #     return self.doc_to_choice(doc)[doc[doc_to_text]]
                # else:
792
793
                return doc[doc_to_text]
            else:
lintangsutawika's avatar
lintangsutawika committed
794
795
796
797
798
                text_string = utils.apply_template(doc_to_text, doc)
                if text_string.isdigit():
                    return ast.literal_eval(text_string)
                else:
                    return text_string
799
        elif callable(doc_to_text):
800
            return doc_to_text(doc)
lintangsutawika's avatar
lintangsutawika committed
801
        # Used when applying a Promptsource template
802
        elif hasattr(doc_to_text, "apply"):
803
804
805
806
807
            applied_prompt = doc_to_text.apply(doc)
            if len(applied_prompt) == 2:
                return applied_prompt[0]
            else:
                eval_logger.warning("Applied prompt returns empty string")
808
                return self._config.fewshot_delimiter
809
        else:
810
            print(type(doc_to_text))
811
            raise TypeError
812

813
    def doc_to_target(self, doc: dict) -> Union[int, str, list]:
814
815
816

        if self.prompt is not None:
            doc_to_target = self.prompt
817
818
819
        else:
            doc_to_target = self._config.doc_to_target

820
821
822
        if type(doc_to_target) == int:
            return doc_to_target
        elif type(doc_to_target) == str:
823
            if doc_to_target in self.features:
824
825
826
827
                # if self._config.doc_to_choice is not None:
                #     return self.doc_to_choice(doc)[doc[doc_to_target]]
                # else:
                return doc[doc_to_target]
828
            else:
lintangsutawika's avatar
lintangsutawika committed
829
830
831
                target_string = utils.apply_template(doc_to_target, doc)
                if target_string.isdigit():
                    return ast.literal_eval(target_string)
lintangsutawika's avatar
lintangsutawika committed
832
833
834
835
836
                elif (
                    len(target_string) >= 2
                    and (target_string[0] == "[")
                    and (target_string[-1] == "]")
                ):
837
                    return ast.literal_eval(target_string)
lintangsutawika's avatar
lintangsutawika committed
838
839
                else:
                    return target_string
840
841
        elif type(doc_to_target) == list:
            return doc_to_target
842
        elif callable(doc_to_target):
843
            return doc_to_target(doc)
lintangsutawika's avatar
lintangsutawika committed
844
        # Used when applying a Promptsource template
845
        elif hasattr(doc_to_target, "apply"):
846
            applied_prompt = doc_to_target.apply(doc)
847
848
849
850
            if len(applied_prompt) == 2:
                return applied_prompt[1]
            else:
                eval_logger.warning("Applied prompt returns empty string")
851
                return self._config.fewshot_delimiter
852
853
        else:
            raise TypeError
854

baberabb's avatar
baberabb committed
855
    def doc_to_choice(self, doc: Any) -> List[str]:
856
857
858

        if self.prompt is not None:
            doc_to_choice = self.prompt
lintangsutawika's avatar
lintangsutawika committed
859
        elif self._config.doc_to_choice is None:
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
            eval_logger.error("doc_to_choice was called but not set in config")
        else:
            doc_to_choice = self._config.doc_to_choice

        if type(doc_to_choice) == str:
            return ast.literal_eval(utils.apply_template(doc_to_choice, doc))
        elif type(doc_to_choice) == list:
            return doc_to_choice
        elif type(doc_to_choice) == dict:
            return list(doc_to_choice.values())
        elif callable(doc_to_choice):
            return doc_to_choice(doc)
        elif hasattr(doc_to_choice, "get_answer_choices_list"):
            return doc_to_choice.get_answer_choices_list(doc)
        else:
            raise TypeError
876

877
    def gold_alias(self, doc):
878
879
880
881
882
        # returns a version of the gold target answer to a document,
        # which should be passed into metric for scoring as the ground truth.

        # in multiple_choice tasks, this should be castable to an int corresponding to the index
        # within the answer choices, while doc_to_target is the string version of {{answer_choices[gold]}}.
lintangsutawika's avatar
lintangsutawika committed
883
        if self._config.gold_alias is not None:
884
885
            doc_to_target = self._config.gold_alias
        else:
lintangsutawika's avatar
lintangsutawika committed
886
            return self.doc_to_target(doc)
887
888
889
890
891
892
893
894
895
896

        if type(doc_to_target) == str:
            return utils.apply_template(doc_to_target, doc)
        elif callable(doc_to_target):
            return doc_to_target(doc)
        elif hasattr(doc_to_target, "apply"):
            return doc_to_target.apply(doc)[1]
        else:
            raise TypeError

baberabb's avatar
baberabb committed
897
898
899
    def construct_requests(
        self, doc: dict, ctx: str, **kwargs
    ) -> Union[List[Instance], Instance]:
900

901
        if self.OUTPUT_TYPE == "loglikelihood":
lintangsutawika's avatar
lintangsutawika committed
902
            arguments = (ctx, self.doc_to_target(doc))
903
        elif self.OUTPUT_TYPE == "loglikelihood_rolling":
lintangsutawika's avatar
lintangsutawika committed
904
            arguments = (self.doc_to_target(doc),)
905
        elif self.OUTPUT_TYPE == "multiple_choice":
906
907

            choices = self.doc_to_choice(doc)
908
            target_delimiter = self._config.target_delimiter
909
910
            if self.multiple_input:
                # If there are multiple inputs, choices are placed in the ctx
911
                cont = self.doc_to_target(doc)
912
                arguments = [(ctx, f"{target_delimiter}{cont}") for ctx in choices]
913
            else:
914
                # Otherwise they are placed in the continuation
915
                arguments = [(ctx, f"{target_delimiter}{cont}") for cont in choices]
916

917
            request_list = [
918
919
                Instance(
                    request_type="loglikelihood",
lintangsutawika's avatar
lintangsutawika committed
920
                    doc=doc,
921
                    arguments=arg,
922
                    idx=i,
923
924
                    **kwargs,
                )
925
                for i, arg in enumerate(arguments)
926
            ]
927
            # TODO: we should raise a warning telling users this will at most ~2x runtime.
928
            if "acc_mutual_info" in self._metric_fn_list.keys():
929
930
931
                # if we are calculating multiple choice accuracy
                # using mutual information instead of raw loglikelihood as metric, need unconditional lls.

lintangsutawika's avatar
lintangsutawika committed
932
                # here mutual info refers to calculating
933
934
935
936
937
938
                # log(P(choice|ctx) / P(choice)) = log(P(choice|ctx)) - log(P(choice))
                # in other words normalizing by subtracting the unconditional logprob of each choice.
                request_list.extend(
                    [
                        Instance(
                            request_type="loglikelihood",
lintangsutawika's avatar
lintangsutawika committed
939
                            doc=doc,
940
                            arguments=("", "{}".format(choice)),
941
942
943
                            idx=i,
                            **kwargs,
                        )
lintangsutawika's avatar
lintangsutawika committed
944
                        for i, choice in enumerate(choices)
945
946
947
                    ]
                )
            return request_list
lintangsutawika's avatar
lintangsutawika committed
948

949
        elif self.OUTPUT_TYPE == "greedy_until":
950
            arguments = (ctx, self._config.generation_kwargs)
lintangsutawika's avatar
lintangsutawika committed
951
952

        return Instance(
lintangsutawika's avatar
lintangsutawika committed
953
954
            request_type=self.OUTPUT_TYPE, doc=doc, arguments=arguments, idx=0, **kwargs
        )
955
956
957

    def process_results(self, doc, results):

lintangsutawika's avatar
lintangsutawika committed
958
959
        if callable(self._config.process_results):
            return self._config.process_results(doc, results)
lintangsutawika's avatar
lintangsutawika committed
960

961
        result_dict = {}
962
        use_metric = list(self._metric_fn_list.keys())
963
964
965
        if self.OUTPUT_TYPE == "loglikelihood":
            results = results[0]
            ll, is_greedy = results
966
967
968
969
            return {
                **({"perplexity": ll} if "perplexity" in use_metric else {}),
                **({"acc": int(is_greedy)} if "acc" in use_metric else {}),
            }
970
        elif self.OUTPUT_TYPE == "loglikelihood_rolling":
haileyschoelkopf's avatar
haileyschoelkopf committed
971
            (loglikelihood,) = results
972
973
            _words = self.count_words(self.doc_to_target(doc))
            _bytes = self.count_bytes(self.doc_to_target(doc))
haileyschoelkopf's avatar
haileyschoelkopf committed
974
            return {
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
                **(
                    {"word_perplexity": (loglikelihood, _words)}
                    if "word_perplexity" in use_metric
                    else {}
                ),
                **(
                    {"byte_perplexity": (loglikelihood, _bytes)}
                    if "byte_perplexity" in use_metric
                    else {}
                ),
                **(
                    {"bits_per_byte": (loglikelihood, _bytes)}
                    if "bits_per_byte" in use_metric
                    else {}
                ),
haileyschoelkopf's avatar
haileyschoelkopf committed
990
            }
991
        elif self.OUTPUT_TYPE == "multiple_choice":
992
993

            lls, is_greedy = zip(*results)
lintangsutawika's avatar
lintangsutawika committed
994

995
            # retrieve choices in List[str] form, to compute choice lengths, etc.
996
            choices = self.doc_to_choice(doc)
997
998
            completion_len = np.array([float(len(i)) for i in choices])

999
1000
            if (
                2 * len(choices) == len(lls)
1001
                and "acc_mutual_info" in self._metric_fn_list.keys()
1002
1003
1004
1005
1006
1007
1008
            ):
                # then we are doing mutual info.
                # this stores the "dryrun" / unconditional answer loglikelihoods
                lls_unconditional = lls[1::2]
                assert len(lls_unconditional) == len(choices)
                # and this stores our "regular" conditional loglikelihoods
                lls = lls[::2]
1009

1010
1011
            pred = np.argmax(lls)
            pred_norm = np.argmax(lls / completion_len)
lintangsutawika's avatar
lintangsutawika committed
1012

1013
1014
            if self.multiple_input:
                gold = self.doc_to_text(doc)
1015
            else:
1016
                gold = self.doc_to_target(doc)
1017
1018
                if type(gold) is str:
                    gold = choices.index(gold)
lintangsutawika's avatar
lintangsutawika committed
1019

1020
            if self.multiple_target:
lintangsutawika's avatar
lintangsutawika committed
1021
1022
                acc = 1.0 if pred in gold else 0.0
                acc_norm = 1.0 if pred_norm in gold else 0.0
1023
                exact_match = int(any([is_greedy[i] for i in gold]))
lintangsutawika's avatar
lintangsutawika committed
1024
1025
1026
            else:
                acc = 1.0 if pred == gold else 0.0
                acc_norm = 1.0 if pred_norm == gold else 0.0
1027
1028
                # TODO: this gets score of 0 on arc_challenge for pythia-70m. need to test that this works properly
                exact_match = int(is_greedy[gold])
1029
1030

            result_dict = {
1031
                **({"acc": acc} if "acc" in use_metric else {}),
1032
1033
                **({"f1": (gold, pred)} if "f1" in use_metric else {}),
                **({"mcc": (gold, pred)} if "mcc" in use_metric else {}),
1034
                **({"acc_norm": acc_norm} if "acc_norm" in use_metric else {}),
1035
                **({"exact_match": exact_match} if "exact_match" in use_metric else {}),
1036
1037
            }

1038
            if "acc_mutual_info" in use_metric:
lintangsutawika's avatar
lintangsutawika committed
1039
1040
1041
                lls_mutual_info = [
                    ll_c - ll_u for ll_c, ll_u in zip(lls, lls_unconditional)
                ]
1042
1043
1044
                acc_mutual_info = 1.0 if np.argmax(lls_mutual_info) == gold else 0.0
                result_dict["acc_mutual_info"] = acc_mutual_info

1045
1046
        elif self.OUTPUT_TYPE == "greedy_until":

1047
            gold = self.doc_to_target(doc)
lintangsutawika's avatar
lintangsutawika committed
1048
            if self._config.doc_to_choice is not None:
lintangsutawika's avatar
lintangsutawika committed
1049
                # If you set doc_to_choice,
lintangsutawika's avatar
lintangsutawika committed
1050
                # it assumes that doc_to_target returns a number.
1051
1052
                choices = self.doc_to_choice(doc)
                gold = choices[gold]
lintangsutawika's avatar
lintangsutawika committed
1053
1054
            else:
                gold = str(gold)
1055

lintangsutawika's avatar
lintangsutawika committed
1056
            result = results[0]
lintangsutawika's avatar
lintangsutawika committed
1057
            for metric in self._metric_fn_list.keys():
1058
1059
1060
1061
1062
1063
1064
1065
                if self.multiple_target:
                    # in the case where we have multiple targets,
                    # return true if any are true
                    # TODO: this may break for multipLe_target, non zero-or-1 metrics
                    scores = []
                    for gold_option in gold:
                        res = self._metric_fn_list[metric](
                            references=[gold_option],
haileyschoelkopf's avatar
haileyschoelkopf committed
1066
                            predictions=[result],
lintangsutawika's avatar
lintangsutawika committed
1067
                            **self._metric_fn_kwargs[metric],
haileyschoelkopf's avatar
haileyschoelkopf committed
1068
                        )
1069
                        if isinstance(res, dict):
haileyschoelkopf's avatar
haileyschoelkopf committed
1070
                            # TODO: this handles the case where HF evaluate returns a dict.
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
                            res = res[metric]
                        scores.append(res)
                    if any(scores):
                        result_score = 1.0
                    else:
                        result_score = 0.0
                else:
                    result_score = self._metric_fn_list[metric](
                        references=[gold],
                        predictions=[result],
                        **self._metric_fn_kwargs[metric],
                    )
                    if isinstance(result_score, dict):
                        # TODO: this handles the case where HF evaluate returns a dict.
                        result_score = result_score[metric]
                result_dict[metric] = result_score
1087
        else:
lintangsutawika's avatar
lintangsutawika committed
1088
1089
            raise ValueError(
                f"Passed invalid output_type '{self.OUTPUT_TYPE}' ! Please use one of ",
1090
                "'loglikelihood', 'loglikelihood_rolling', 'greedy_until' or 'multiple_choice'",
1091
            )
1092
1093
1094
1095
1096
1097
1098

        return result_dict

    def aggregation(self):
        return self._aggregation_list

    def higher_is_better(self):
haileyschoelkopf's avatar
haileyschoelkopf committed
1099
        return self._higher_is_better
1100
1101
1102
1103
1104


class MultipleChoiceTask(Task):
    OUTPUT_TYPE: str = "loglikelihood"

baberabb's avatar
baberabb committed
1105
    def doc_to_target(self, doc: dict) -> str:
1106
1107
        return " " + doc["choices"][doc["gold"]]

baberabb's avatar
baberabb committed
1108
    def construct_requests(self, doc: dict, ctx: str, **kwargs) -> List[Instance]:
1109
        # TODO: add mutual info here?
lintangsutawika's avatar
lintangsutawika committed
1110
1111
        return [
            Instance(
haileyschoelkopf's avatar
haileyschoelkopf committed
1112
                request_type="loglikelihood",
lintangsutawika's avatar
lintangsutawika committed
1113
                doc=doc,
1114
                arguments=(ctx, " {}".format(choice)),
1115
                idx=i,
1116
1117
                **kwargs,
            )
lintangsutawika's avatar
lintangsutawika committed
1118
1119
            for i, choice in enumerate(doc["choices"])
        ]
1120

baberabb's avatar
baberabb committed
1121
    def process_results(self, doc: dict, results: List[Tuple[float, bool]]) -> dict:
lintangsutawika's avatar
lintangsutawika committed
1122
1123
1124
        results = [
            res[0] for res in results
        ]  # only retain loglikelihoods, discard is_greedy TODO: do we need is_greedy anywhere?
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
        gold = doc["gold"]

        acc = 1.0 if np.argmax(results) == gold else 0.0
        completion_len = np.array([float(len(i)) for i in doc["choices"]])
        acc_norm = 1.0 if np.argmax(results / completion_len) == gold else 0.0

        return {
            "acc": acc,
            "acc_norm": acc_norm,
        }

baberabb's avatar
baberabb committed
1136
    def higher_is_better(self) -> dict:
1137
1138
1139
1140
1141
        return {
            "acc": True,
            "acc_norm": True,
        }

baberabb's avatar
baberabb committed
1142
    def aggregation(self) -> dict:
1143
1144
1145
1146
1147
1148
        return {
            "acc": mean,
            "acc_norm": mean,
        }


lintangsutawika's avatar
lintangsutawika committed
1149
class PerplexityTask(Task):
1150
1151
    OUTPUT_TYPE = "loglikelihood_rolling"

baberabb's avatar
baberabb committed
1152
    def has_training_docs(self) -> bool:
1153
1154
        return False

baberabb's avatar
baberabb committed
1155
    def fewshot_examples(self, k: int, rnd) -> List:
1156
1157
1158
        assert k == 0
        return []

baberabb's avatar
baberabb committed
1159
    def fewshot_context(self, doc: dict, num_fewshot: int) -> Literal[""]:
1160
1161
1162
1163
1164
1165
        assert (
            num_fewshot == 0
        ), "The number of fewshot examples must be 0 for perplexity tasks."

        return ""

baberabb's avatar
baberabb committed
1166
    def higher_is_better(self) -> dict:
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
        return {
            "word_perplexity": False,
            "byte_perplexity": False,
            "bits_per_byte": False,
        }

    def doc_to_decontamination_query(self, doc):
        return doc

    def doc_to_text(self, doc):
        return ""

    def doc_to_target(self, doc):
        return doc

baberabb's avatar
baberabb committed
1182
    def construct_requests(self, doc: dict, ctx: Union[str, None], **kwargs):
1183
1184
        assert not ctx

lintangsutawika's avatar
lintangsutawika committed
1185
1186
1187
1188
1189
1190
1191
        return Instance(
            request_type=self.OUTPUT_TYPE,
            doc=doc,
            arguments=(self.doc_to_target(doc),),
            idx=0,
            **kwargs,
        )
1192

baberabb's avatar
baberabb committed
1193
    def process_results(self, doc: dict, results: float) -> dict:
1194
        (loglikelihood,) = results
haileyschoelkopf's avatar
haileyschoelkopf committed
1195
1196
        words = self.count_words(self.doc_to_target(doc))
        bytes_ = self.count_bytes(self.doc_to_target(doc))
1197
1198
1199
1200
1201
1202
        return {
            "word_perplexity": (loglikelihood, words),
            "byte_perplexity": (loglikelihood, bytes_),
            "bits_per_byte": (loglikelihood, bytes_),
        }

baberabb's avatar
baberabb committed
1203
    def aggregation(self) -> dict:
1204
1205
1206
1207
1208
1209
1210
        return {
            "word_perplexity": weighted_perplexity,
            "byte_perplexity": weighted_perplexity,
            "bits_per_byte": bits_per_byte,
        }

    @classmethod
baberabb's avatar
baberabb committed
1211
    def count_bytes(cls, doc) -> int:
1212
1213
1214
        return len(doc.encode("utf-8"))

    @classmethod
baberabb's avatar
baberabb committed
1215
    def count_words(cls, doc) -> int:
1216
1217
        """Downstream tasks with custom word boundaries should override this!"""
        return len(re.split(r"\s+", doc))